In [1]:

    

import cPickle
import numpy as np
import matplotlib.pyplot as plt

# PARAMS
log_dir = "/home/sforesti/avakas/scratch/sforestier001/logs/CogSci2017/2017-01-17_19-32-17-EXPLO-0.5"


filename = log_dir + '/results/progress.pickle'
with open(filename, 'r') as f:
    data_progress = cPickle.load(f)


    

In [8]:

    

%matplotlib inline
import seaborn
n_trials = 500
n_iter = 80000
iter_ds = 100
trial_list = range(1,n_trials + 1) 
config_name = "RMB"

x = [iter_ds*i for i in range(n_iter/iter_ds)]

for trial in [5]:
    for mid in data_progress[config_name][trial]["chosen_modules"].keys():
        print trial, mid, data_progress[config_name][trial]["cp_evolution"][mid][-1]
        plt.plot(x, data_progress[config_name][trial]["cp_evolution"][mid], label=mid)
        
plt.ylim([0, 0.5])       
plt.xlim([0, n_iter])  
plt.legend(ncol=3)


    

    




5 mod1 0.135940935606
5 mod2 0.0309201612843
5 mod3 -0.021812501007
5 mod4 -0.0570813854884
5 mod5 -0.0433058840338
5 mod6 0.0167809601794
5 mod12 -0.0660284888172
5 mod13 0.137381025481
5 mod10 -0.0581517651252
5 mod11 -0.0698186307274






    
Out[8]:





<matplotlib.legend.Legend at 0x7f3ebae61f90>






    

In [10]:

    

for trial in [6]:
    for mid in data_progress[config_name][trial]["chosen_modules"].keys():
        print trial, mid, data_progress[config_name][trial]["pp_evolution"][mid][-1]
        plt.plot(x, data_progress[config_name][trial]["pp_evolution"][mid], label=mid)
        
plt.ylim([0, 0.5])       
plt.xlim([0, n_iter])  
plt.legend(ncol=3)


    

    




6 mod1 0.0603036296494
6 mod2 0.00779648397568
6 mod3 0.00591827175162
6 mod4 -0.0046990959403
6 mod5 -0.0118002951563
6 mod6 -0.0277905304687
6 mod12 0.0365006097704
6 mod13 0.0995792961963
6 mod10 0.0381196194555
6 mod11 0.0122298882434






    
Out[10]:





<matplotlib.legend.Legend at 0x7f3ee4ce7410>